Generative Bayesian Hyperparameter Tuning

• URL: http://arxiv.org/abs/2512.20051v1
• Date: Tue, 23 Dec 2025 05:00:52 GMT
• Title: Generative Bayesian Hyperparameter Tuning
• Authors: Hedibert Lopes, Nick Polson, Vadim Sokolov,
• Abstract summary: Cross-validation is often computationally prohibitive at scale, while fully Bayesian hyper- parameter learning can be difficult due to the cost of posterior sampling.<n>We develop a generative perspective that combines two ideas: (i) optimization-based approximations to Bayesian posteriors via randomized, weighted objectives (weighted Bayesian bootstrap) and (ii) repeated optimization across many hyper- parameter settings.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: \noindent Hyper-parameter selection is a central practical problem in modern machine learning, governing regularization strength, model capacity, and robustness choices. Cross-validation is often computationally prohibitive at scale, while fully Bayesian hyper-parameter learning can be difficult due to the cost of posterior sampling. We develop a generative perspective on hyper-parameter tuning that combines two ideas: (i) optimization-based approximations to Bayesian posteriors via randomized, weighted objectives (weighted Bayesian bootstrap), and (ii) amortization of repeated optimization across many hyper-parameter settings by learning a transport map from hyper-parameters (including random weights) to the corresponding optimizer. This yields a ``generator look-up table'' for estimators, enabling rapid evaluation over grids or continuous ranges of hyper-parameters and supporting both predictive tuning objectives and approximate Bayesian uncertainty quantification. We connect this viewpoint to weighted $M$-estimation, envelope/auxiliary-variable representations that reduce non-quadratic losses to weighted least squares, and recent generative samplers for weighted $M$-estimators.

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